Unifying models have shown that the amount of space used by animals (e.g., activity space, home range) scales allometrically with body mass for terrestrial taxa; however, such relationships are far less clear for marine species. We compiled movement data from 1,596 individuals across 79 taxa collected using a continental passive acoustic telemetry network of acoustic receivers to assess allometric scaling of activity space. We found that ectothermic marine taxa do exhibit allometric scaling for activity space, with an overall scaling exponent of 0.64. However, body mass alone explained only 35% of the variation with the remaining variation best explained by trophic position for teleosts and latitude for sharks, rays, and marine reptiles. Taxon-specific allometric relationships highlighted weaker scaling exponents among teleost fish species (0.07) than sharks (0.96), rays (0.55), and marine reptiles (0.57). The allometric scaling relationship and scaling exponents for the marine taxonomic groups examined were lower than those reported from studies that had collated both marine and terrestrial species data derived using various tracking methods. We propose that these disparities arise because previous work integrated summarised data across many studies that used differing methods for collecting and quantifying activity space, introducing considerable uncertainty into slope estimates. Our findings highlight the benefit of using large-scale, coordinated animal biotelemetry networks to address cross-taxa evolutionary and ecological questions.

Metrics of activity space used to examine allometric scaling relationships for marine animals were calculated from acoustic telemetry detection data of tagged organisms collated over a decade (2007–2017) by the IMOS Animal Tracking Facility and stored in an openly accessible data repository (https://animaltracking.aodn.org.au). Data available in the repository undergoes strict quality controls, eliminating erroneous raw detections using specific rules that interrogate each data point in light of spatio-temporal patterns in detection and known species ranges.

Raw detections were processed to estimate short-term centres of activity (COA) for 60-minute intervals. COAs were estimated prior to subsequent activity space estimation, to account for i) the varied transmission settings across tagging projects, ii) spatial biases in raw detection data inherent in node-based telemetry studies, and iii) to incorporate movements of tagged animals between fixed receivers (Udyawer et al. 2018). We conducted a second round of data filtering, by excluding individuals that were not detected at five unique COA locations. A minimum of 5 unique COA locations meant that tagged individuals recorded on a minimum of 2 receivers were included, provided sufficient movements were recorded between the receivers over the tracking period. This process ensured subsequent estimates of activity space were not biased based on lack of positional data. Detection data were used to calculate standardised metrics of activity space using the Animal Tracking Toolbox functions within the R package ‘VTrack’ (Campbell et al. 2012; Udyawer et al. 2018), which allowed for direct comparison across taxa and sites. For each tagged individual, activity space included the area within the 95% contour of a utilisation distribution estimated using a Brownian bridge movement model including all detections (Horne et al. 2007). A uniform smoothing parameter associated with the listening range of acoustic telemetry methods was applied to all the data to ensure comparability. The smoothing parameter for Brownian bridge models associated with relocation error (σ₂) was determined using an ad hoc approach (Kie 2013) and aligned with measured listening ranges of acoustic arrays within the IMOS network (e.g., Knip et al. 2012; Matley et al. 2015). The smoothing parameter associated with animal speed (σ1) was estimated using a maximum likelihood estimator following methods outlined by Calenge (2006).

We collated species identity, release location, body mass, trophic group, and foraging habitat data for each tagged individual. Each species used was classified into one of four broad taxonomic groupings: teleost fish, shark, ray, or marine reptile. Where information on body mass was not recorded, it was estimated from total length, snout-vent length, or carapace length using length-weight relationships in published literature (Froese and Pauly 2017; Froese et al. 2014; Hirth 1982; Webb and Messel 1978). Trophic level indices for each species were sourced from published literature that used standardised indices based on diet composition (Cortés 1999; Froese and Pauly 2017; Jacobsen and Bennett 2013). Each individual was then categorised into one of three broad trophic groups: Primary Consumers, Secondary Consumers, and Tertiary Consumers. Foraging habitat of each species was assessed by classifying each species as either pelagic or benthic foragers. These data are provided here in a tabulated comma-separated values file (2020-07-09_Scaling paper QCdata.csv).

Taxonomic relationships between species for phylogenetic corrections were extracted from the Open Tree of Life Taxonomy database (OTL; https://tree.opentreeoflife.org). The OTL project assembles current phylogenetic relationships across all organisms on earth by synthesising published phylogenetic trees across multiple taxonomies (Hinchliff et al. 2015). The phylogenetic tree utilised in this study was built by sub-setting the OTL dataset for the species for which activity space data was available, and synthesising a single phylogenetic tree. Accessing the OTL dataset, sub-setting and building the phylogenetic tree used in this analysis was conducted using the ‘rotl’ R package (Michonneau et al. 2016). These data are provided here as a text file with phylogenetic tree data represented in Newick notation (2020-07-09_OTOL_AllQCSpecies_Phylodata.txt).

References:

• Calenge, C. 2006. The package "adehabitat" for the R software: A tool for the analysis of space and habitat use by animals. Ecological Modelling 197:516-519.
• Campbell, H. A., M. E. Watts, R. G. Dwyer, and C. E. Franklin. 2012. V-Track: software for analysing and visualising animal movement from acoustic telemetry detections. Marine and Freshwater Research 63:815-820.
• Cortés, E. 1999. Standardized diet compositions and trophic levels of sharks. ICES Journal of marine science 56:707-717.
• Froese, R., and D. Pauly. 2017. Fishbase (version 06/2017) http://www.fishbase.org.
• Froese, R., J. T. Thorson, and R. Reyes. 2014. A Bayesian approach for estimating length‐weight relationships in fishes. Journal of Applied Ichthyology 30:78-85.
• Hinchliff, C. E., S. A. Smith, J. F. Allman, J. G. Burleigh, R. Chaudhary, L. M. Coghill, K. A. Crandall et al. 2015. Synthesis of phylogeny and taxonomy into a comprehensive tree of life. Proceedings of the National Academy of Sciences 112:12764-12769.
• Hirth, H. F. 1982. Weight and length relationships of some adult marine turtles. Bulletin of Marine Science 32:336-341.
• Horne, J. S., E. O. Garton, S. M. Krone, and J. S. Lewis. 2007. Analysing animal movements using brownian bridges. Ecology 88:2354-2363.
• Jacobsen, I. P., and M. B. Bennett. 2013. A comparative analysis of feeding and trophic level ecology in stingrays (Rajiformes; Myliobatoidei) and electric rays (Rajiformes: Torpedinoidei). PloS one 8:e71348.
• Kie, J. G. 2013. A rule-based ad hoc method for selecting a bandwidth in kernel home-range analyses. Animal Biotelemetry 1:1-12.
• Michonneau, F., J. W. Brown, and D. J. Winter. 2016. rotl: an R package to interact with the Open Tree of Life data. Methods in Ecology and Evolution 7:1476-1481.

Datasets are provided in comma-separated values and text formats to be uploaded onto any statistical software to conduct data visualisations and analyses. Phylogenetic tree data can be input and visualised using software that reads phylogenetic tree data in parenthetic Newick notation (New Hampshire tree format).